Tag Archives: Algorithms

Behavioral redefinition

Vice reports on a Tokyo-based company, DeepScore, pitching software for the automatic recognition of ‘trustworthiness’, e.g. in loan applicants. Although their claimed false-negative rate of 30% may not sound particularly impressive, it must of course be compared to well-known human biases in lending decisions. Perhaps more interesting is the instrumentalization cycle, which is all but assured to take place if DeepScore’s algorithm gains wide acceptance. On the one hand, the algorithm’s goal is to create a precise definition for a broad and vague human characteristic like trustworthiness—that is to say, to operationalize it. Then, if the algorithm is successful on its training sample and becomes adopted by real-world decision-makers, the social power of the adopters reifies the research hypothesis: trustworthiness becomes what the algorithm says it is (because money talks). Thus, the behavioral redefinition of a folk psychology concept comes to fruition. On the other hand, however, instrumentalization immediately kicks in, as users attempt to game the operationalized definition, by managing to present the algorithmically-approved symptoms without the underlying condition (sincerity). Hence, the signal loses strength, and the cycle completes. The fact that DeepScore’s trustworthiness algorithm is intended for credit markets in South-East Asia, where there exist populations without access to traditional credit-scoring channels, merely clarifies the ‘predatory inclusion’ logic of such practices (v. supra).

Trustworthiness of unfree code

Several reports are circulating (e.g., via /.) of a court case in New Jersey in which the defendant won the right to audit proprietary genetic testing software for errors or potential sources of bias. It being a murder trial, this is about as close to a life-or-death use-case as possible.

Given the stakes, it is understandable that a low-trust standard should prevail in  forensic matters, rendering an audit indispensable (nor is the firm’s “complexity defence” anything short of untenable). What is surprising, rather, is how long it took to obtain this type of judicial precedent. The authoritativeness deficit of algorithms is a topic of burning intensity generally; that in such a failure-critical area a business model based on proprietary secrecy has managed to survive is truly remarkable. It is safe to say that this challenge will hardly be the last. Ultimately, freely auditable software would seem to be the superior systemic answer for this type of applications.

Free speech and monetization

Yesterday, I attended an Electronic Frontier Foundation webinar in the ‘At Home with EFF’ series on Twitch: the title was ‘Online Censorship Beyond Trump and Parler’. Two panels hosted several veterans and heavyweights in the content moderation/trust & safety field, followed by a wrap-up session presenting EFF positions on the topics under discussion.

Several interesting points emerged with regard to the interplay of market concentration, free speech concerns, and the incentives inherent in the dominant social media business model. The panelists reflected on the long run, identifying recurrent patterns, such as the economic imperative driving infrastructure companies from being mere conduits of information to becoming active amplifiers, hence inevitably getting embroiled in moderation. While neutrality and non-interference may be the preferred ideological stance for tech companies, at least publicly, editorial decisions are made a necessity by the prevailing monetization model, the market for attention and engagement.

Perhaps the most interesting insight, however, emerged from the discussion of the intertwining of free speech online with the way in which such speech is (or is not) allowed to make itself financially sustainable. Specifically, the case was made for the importance of the myriad choke points up and down the stack where those who wish to silence speech can exert pressure: if cloud computing cannot be denied to a platform in the name of anti-discrimination, should credit card verification or merch, for instance, also be protected categories?

All in all, nothing shockingly novel; it is worth being reminded, however, that a wealth of experience in the field has already accrued over the years, so that single companies (and legislators, academics, the press, etc.) need not reinvent the wheel each time trust & safety or content moderation are on the agenda.

Reddit mobs rampaging on the stockmarket

I am following (just like everyone else) the developing GameStop story. Beyond the financial technicalities, what is interesting for present purposes is that the dynamics of internet virality seem to be finding a close parallel in stock valuation. The term “meme stock” is telling. In other words, at present the online coordination mechanisms, the capital, and the nihilistic boredom are all available to craft an alternative description of reality, which in turn is self-reinforcing (until it isn’t).

Hiding

Given the recent salience of news on surveillance and surveillance capitalism, it is to be expected that there would be rising interest in material, technical countermeasures. Indeed, a cottage industry of surveillance-avoidance gear and gadgetry has sprung up. The reviews of these apparatuses tend to agree that the results they achieve are not great. For one thing, they are typically targeted at one type of surveillance vector at a time, thus requiring a specifically tailored attack model rather than being comprehensive solutions. Moreover, they can really only be fine-tuned properly if they have access to the source code of the algorithm they are trying to beat, or at least can test its response in controlled conditions before facing it in the wild. But of course, uncertainty about the outcomes of surveillance, or indeed about whether it is taking place to begin with, is the heart of the matter.

The creators of these countermeasures themselves, whatever their personal intellectual commitment to privacy and anonymity, hardly follow their own advice in eschewing the visibility the large internet platforms afford. Whether these systems try to beat machine-learning algorithms through data poisoning or adversarial attacks, they tend to be more of a political statement and proof of concept than a workable solution, especially in the long term. In general, even when effective, using these countermeasures is seen as extremely cumbersome and self-penalizing: they can be useful in limited situations for operating in ‘stealth mode’, but cannot be lived in permanently.

If this is the technological state of play, are we destined to a future of much greater personal transparency, or is the notion of hiding undergoing an evolution? Certainly, the momentum behind the diffusion of surveillance techniques such as facial recognition appears massive worldwide. Furthermore, it is no longer merely a question of centralized state agencies: the technology is mature for individual consumers to enact private micro-surveillance. This sea change is certainly prompting shifts in acceptable social behavior. But as to the wider problem of obscurity in our social lives, the strategic response may well lie in a mixture of compartimentalization and hiding in plain sight. And of course systems of any kind are easier to beat when one can target the human agent at the other end.